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arXiv 2020年

作者： Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 Peng Cheng Laboratory Shenzhen518055 China

As an effective learning paradigm against insufficient training samples, Multi-Task Learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge management

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Real-time reversible data hiding with shifting block histogram of pixel differences in encrypted image

Real-time reversible data hiding with shifting block histogr...

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Journal of Real-Time Image Processing

作者： Tang, Zhenjun Xu, Shijie Ye, Dengpan Wang, Jinyan Zhang, Xianquan Yu, Chuanqiang Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China School of Computer Science and Information Technology Guangxi Normal University No. 15 YuCai Road Guilin541004 China

Reversible data hiding in encrypted image (RDH-EI) is a hot topic of data hiding in recent years. Most RDH-EI algorithms do not reach desirable embedding rate and their computational costs are not suitable for real-time applications. Aiming at these problems, we propose an efficient RDH-EI algorithm with shifting block histogram of pixel differences in homomorphic encrypted domain. A key step of our RDH-EI algorithm is the block-based encryption scheme with additive homomorphism, which can preserve spatial correlation of plaintext image in homomorphic encrypted domain. In addition, our proposed technique of shifting block histogram can achieve efficient data embedding with high payload and correctly recover image. Extensive experiments are conducted to validate performance of our RDH-EI algorithm. Comparison results illustrate that our RDH-EI algorithm outperforms some state-of-the-art algorithms in terms of embedding rate, visual quality and computational time. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cryptography Graphic methods Image processing Pixels Steganography Computational costs Computational time Histogram shifting Homomorphic cryptosystem Real time application Reversible data hiding Spatial correlations State of the art algorithms

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Ensuring data confidentiality via plausibly deniable encryption and secure deletion-a survey

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cybersecurity 2018年第1期1卷 106-125页

作者： Qionglu Zhang Shijie Jia Bing Chang Bo Chen Data Assurance and Communication Security Research Center Chinese Academy of SciencesBeijingChina State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina School of Information Systems Singapore Management UniversitySingaporeSingapore Department of Computer Science Michigan Technological UniversityHoughtonUSA

Ensuring confidentiality of sensitive data is of paramount importance,since data leakage may not only endanger data owners’privacy,but also ruin reputation of businesses as well as violate various regulations like HIPPA and Sarbanes-Oxley *** provide confidentiality guarantee,the data should be protected when they are preserved in the personal computing devices(i.e.,confidentiality during their lifetime);and also,they should be rendered irrecoverable after they are removed from the devices(i.e.,confidentiality after their lifetime).Encryption and secure deletion are used to ensure data confidentiality during and after their lifetime,*** work aims to perform a thorough literature review on the techniques being used to protect confidentiality of the data in personal computing devices,including both encryption and secure *** for encryption,we mainly focus on the novel plausibly deniable encryption(PDE),which can ensure data confidentiality against both a coercive(i.e.,the attacker can coerce the data owner for the decryption key)and a non-coercive attacker.

关键词： Data confidentiality Plausibly deniable encryption Secure deletion

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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific reports 2025年第1期15卷 19223页

作者： Khan Muhammad Adnan Taher M Ghazal Muhammad Saleem Muhammad Sajid Farooq Chan Yeob Yeun Munir Ahmad Sang-Woong Lee Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India. Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan. Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates. chan.yeun@ku.ac.ae. University College Korea University Seoul 02841 Republic of Korea. Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 Republic of Korea. slee@gachon.ac.kr.

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model's predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered "black box" systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

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Initial ASEAN digital security and interoperability standard proposal

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AIP Conference Proceedings 2023年第1期2739卷

作者： Sangsuree Vasupongayya Gunawan Hutagalung Obrina Candra Chalee Vorakulpipat Doni Purnomo Parinda Wattanasri Eko Supriyanto Shukor Abd Razak Othman Ibrahim Anazida Zainal Sri Devi Ravana Hiep Cong Pham Tharith Sriv Daw Khin Myo Su Mora Hertanto Agus Winarno Prabaswari Raden Muhammad Taufik Yuniantoro Badrus Zaman Fatmawati Retna Apsari Yutika Amelia Effendi Norjihan Abdul Ghani Zarina Shukur Hamidah Ibrahim Nur Izura Udzir Marnel Peradilla Jude Michael Teves Nestor Michael Tiglao Cenon Alfonso Pg Emeroylariffion Abas Mueen Uddin Agustin Kusumayati Brian Sri Prahastuti Department of Computer Engineering Faculty of Engineering Prince of Songkla University Hatyai Songkhla 90110 THAILAND Directorate General of Post and Information Technology Ministry of Communication and Information Technology Jakarta INDONESIA Politeknik Siber dan Sandi Negara National Cyber & Crypto Agency (BSSN) Depok West Java INDONESIA Information Security Research Team at National Electronics and Computer Technology Center Bangkok THAILAND National Standardization Agency of Indonesia Jakarta INDONESIA Department of Disease Control Ministry of Public Health Mueang Nonthaburi Nonthaburi THAILAND School of Biomedical Engineering and Health Sciences Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA IJN-UTM Cardiovascular Engineering Center Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur MALAYSIA RMIT University Vietnam Ho Chi Minh City VIETNAM Royal University of Phnom Penh Phnom Penh CAMBODIA Yangon University of Economics Yangon MYANMAR Agency for the Assessment and Application of Technology (BPPT) Jakarta INDONESIA Faculty of Science and Technology Universitas Airlangga Surabaya East Java INDONESIA Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya East Java INDONESIA Centre for Cyber Security Universiti Kebangsaan Malaysia Bangi Selangor MALAYSIA Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor MALAYSIA Computer Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Software Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City P

In this work, the result of the initial attempt to propose an Association of Southeast Asian Nations (ASEAN) digital security and interoperability standard to support the mobility among ASEAN countries is presented. Literature reviews on existing coronavirus disease 2019 (COVID-19) related digital technologies of six ASEAN member countries are conducted. Several concerns related to creating/implementing the ASEAN digital security and interoperability standard are discussed including the technology accessing concern, the data security and privacy concern, and the data interoperability and availability concern. The proposed solution toward creating an ASEAN digital security and interoperability standard is then presented. To evaluate the proposed solution, a survey has been conducted on representatives of nine ASEAN countries on four main issues including the security standards and requirements, the interoperability standard and requirements, the certifications, and the implementation challenges. According to the survey and the discussion, seven recommendations were proposed toward creating an ASEAN digital security and interoperability standard. First, each country collects and maintains the data of its own citizens and also provides a method to authenticate and access the data by authorized requests. Second, QR code standard or ASEAN QR code standard must be agreed upon so that it can be authenticated and verified by ASEAN countries. Third, health risk assessment must be agreed upon to be used. Forth, the security standard, the data, the data format and the exchange protocol must be agreed upon at the ASEAN policy level while adoption of international standards as an implementation guideline will ease of the process and neutralize different requirements from various countries. Fifth, any accountability of incidents must be agreed upon at the ASEAN policy level. Sixth, certification might be extended to certification of risk assessment for organizations, products or

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Understanding the Behavioral Differences Between American and German Users: A Data-Driven Study

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Big Data Mining and Analytics 2018年第4期1卷 284-296页

作者： Chenxi Yang Yang Chen Qingyuan Gong Xinlei He Yu Xiao Yuhuan Huang Xiaoming Fu School of Computer Science Fudan UniversityShanghai 200433China Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of EducationShanghai 200433China Department of Communications and Networking Aalto University02150 EspooFinland Faculty of European Languages and Cultures Guangdong University of Foreign StudiesGuangzhou 510420China Institute of Computer Science University of Gottingen37077 GottingenGermany

Given that the USA and Germany are the most populous countries in North America and Western Europe,understanding the behavioral differences between American and German users of online social networks is *** this work,we conduct a data-driven study based on the Yelp Open *** demonstrate the behavioral characteristics of both American and German users from different aspects,i.e.,social connectivity,review styles,and spatiotemporal *** addition,we construct a classification model to accurately recognize American and German users according to the behavioral *** model achieves high classification performance with an F1-score of 0.891 and AUC of 0.949.

关键词： behavioral difference online social networks Yelp machine learning

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Distortion-free watermarking scheme for compressed data in columnar database 15

Distortion-free watermarking scheme for compressed data in c...

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15th International Joint Conference on e-Business and Telecommunications, ICETE 2018

作者： Yaqub, Waheeb Kamel, Ibrahim Aung, Zeyar Center for Cyber Security New York University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science Khalifa University of Science and Technology Masdar Institute Abu Dhabi United Arab Emirates

ISBN: (纸本)9789897583193

Digital watermarking is an effective technique for protecting various databases against data copyright infringement, piracy, and data tampering in the columnar database. Increasing deployments of various database systems and their versatile applications have raised the need for better watermarking schemes that are tailored to the target database systems’ specific architecture. Most of the existing digital watermarking schemes do not take into consideration the side effects that watermarking might have on the database’s important characteristics such as data compression and overall performance. In this research, we propose a distortion-free fragile watermarking scheme for columnar database architecture without interfering its underlying data compression scheme and its overall performance. The proposed scheme is flexible and can be adapted to various distributions of data. We tested our proposed scheme on both synthetic and real-world data, and proved its effectiveness. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： Database systems

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A Modified Fuzzy Fingerprint Vault Based on Pair-Polar Minutiae Structures 13th

A Modified Fuzzy Fingerprint Vault Based on Pair-Polar Minut...

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13th International Conference on Information securityand cryptology, Inscrypt 2017

作者： Li, Xiangmin Ding, Ning Lu, Haining Gu, Dawu Wang, Shanshan Xu, Beibei Yuan, Yuan Yan, Siyun Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China State Key Laboratory of Cryptology P.O. Box 5159 Beijing100878 China School of Cyber Security Shanghai Jiao Tong University Shanghai China Information Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9783319751597

Biometrics-based cryptosystems are helpful tools facilitating our daily life. Examples are that people use their biometrics to identify themselves in clouds or protect passwords as well as other privacy information in mobile phones connected to Internet-of-Things. Among various biometrics, fingerprints are one used most widely. A promising type of secure cryptosystems based on fingerprints is the so called fuzzy fingerprint vaults which can bind a secret key to one fingerprint template and release the same key later with the help of a similar but not necessarily fully identical fingerprint template. However, some aspects such as the security and matching accuracy of existing fingerprint-based fuzzy vaults are still not satisfactory due to troublesome issues such as the alignment of fingerprints and minutiae quantization. In this paper we propose a modified fuzzy fingerprint vault based on pair-polar minutiae structures, aiming at achieving better performance. Recall that Li et al. (2016) presented a fuzzy vault based on pair-polar minutiae structures with two-level secure sketch in recovering the secret key. Unlike their scheme, we compress the two-level structure into one level in which minutia descriptors are exploited to match a vault and thus lower the time complexity of decoding. Our experiment evaluation shows that the proposed fuzzy fingerprint vault achieves high matching accuracy. Importantly, our vault is of strong security against existing attacks such as the brute-force attack, false-accept attack and correlation attack. Also this vault adopts the polynomial representation introduced by Dodis and Reyzin (2006) which is thus able to reduce the storage complexity. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Biometrics

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Stance-In-Depth Deep Neural Approach to Stance Classification

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Procedia computer science 2018年 132卷 1646-1653页

作者： Gayathri Rajendran Bhadrachalam Chitturi Prabaharan Poornachandran Dept. of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kerala India Amrita Center for Cyber Security & Networks

Understanding the user intention from text is a problem of growing interest. The social media like Twitter, Facebook etc. extract user intention to analyze the behaviour of a user which in turn is employed for bot recognition, satire detection, fake news detection etc.. The process of identifying stance of a user from the text is called stance detection. This article compares the headline and body pair of a news article and classifies the pair as related or unrelated. The related pair is further classified into agree, disagree, discuss. We call related as detailed classification and unrelated as broad classification. We employ deep neural nets for feature extraction and stance classification. RNN models and its extensions showed significant variations in the classification of detailed class. Bidirectional LSTM model achieved the best accuracy for broad as well as detailed classification.

关键词： Stance Classification deep learning RNN LSTM Gated Recurrent Unit (GRU) Bidirectional word2vec

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A blockchain-based architecture framework for secure sharing of personal health data 20

A blockchain-based architecture framework for secure sharing...

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20th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2018

作者： Amofa, Sandro Sifah, Emmanuel Boateng Obour Agyekum, Kwame O.-B. Abla, Smahi Xia, Qi Gee, James C. Gao, Jianbin School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Center for Cyber Security University of Electronic Science and Technology of China Chengdu China Department of Radiology University of Pennsylvania Philadelphia United States School of Resource and Enviroment Center for Digital Health University of Electronic Science and Technology China

ISBN: (纸本)9781538642948

Health information exchanges have been popular for some time with their advantages known and widely researched. In spite of their utility in increasing provider efficiency and decreasing administrative costs, one challenge that has persisted is the data owners inability to control data after transmission. The lack of technical mechanisms to effectively control patients' health data in the network significantly affects participation of health and medical institutions while perpetrating the silo-based data management that locks value and potential inherent in the data. This not only affects researchers due to the lack of data for research and analysis but the quality of life of *** present a blockchain-supported architectural framework for secure control of personal data in a health information exchange by pairing user-generated acceptable use policies with smart contracts. We highlight the merits of our system, its user-centric focus and also show experimental results along with directions for extending our work. The framework introduces minimal risk to data by architecting a mechanism for controlling data after sharing. In adopting our framework, health service providers can deliver a stronger assurance for data management than is possible with current systems. © 2018 IEEE.

关键词： Blockchain

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